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@article{145563,
        author = {C.Amararaju and Dr. k.venkatramana},
        title = {Detect miscategorized apps using FRAC+Model},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {515-517},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145563},
        abstract = {A continuous test in the quickly advancing application advertise biological system is to keep up the honesty of application classifications. At the time of enlistment, application designers need to choose, what they accept, is the most proper classification for their applications. Other than the intrinsic uncertainty of choosing the correct classification, the approach leaves open the likelihood of abuse and potential gaming by the registrant. Intermittently the application store will refine the rundown of classes accessible and possibly reassign the applications. In any case, it has been  watched that the jumble between the depiction of the application and the class it has a place with, keeps on holding on. Albeit a few regular systems exist, they restrict the reaction time to identify miscategorized applications what's more, still open the test on arrangement. We present FRAC+: (FR)amework for (A)pp (C)ategorization. FRAC+ has the following notable highlights: it depends on an information driven point demonstrate and consequently proposes the classifications suitable for the application store, and it can identify miscategorizated applications. Broad investigations confirm the execution of FRAC+. Investigations on GOOGLE Play demonstrates that FRAC+'s subjects are more lined up with GOOGLE's new classifications and 0.35%-1.10% diversion applications are distinguished to be miscategorized.},
        keywords = {FRAC, App Categorization, miscategorizated application},
        month = {},
        }